US 11,109,103 B2 · Systems and methods for deep recommendations using signature analysis

Abstract

Systems and methods are described herein for providing content item recommendations based on a video. Using feature vectors corresponding to at least one frame of a video (e.g., generated based on texture and shape intensity of a frame), a recommendation system improves content recommendation using analytic and quantitative characteristics derived from a frame of a content item rather than merely manually labeled bibliographic data (e.g., a genre or producer). The recommendation system may generate a feature vector based on a texture, a shape intensity (e.g., generated from a Generalized Hough Transform), and temporal data corresponding to at least one frame of a video. The feature vector is analyzed using a machine learning model (e.g., a neural network) to produce a machine learning model output. The recommendation system causes a recommended content item to be provided based on the machine learning model output.

Claims21

Dependent claims

A method for providing content item recommendations based on a video, the method comprising:

determining, using control circuitry, a texture associated with at least one frame of the video;
transforming, using the control circuitry, the at least one frame of the video to generate a shape intensity;
generating, using the control circuitry, a feature vector based on the texture, the shape intensity, and temporal data corresponding to the at least one frame of the video;
analyzing, using the control circuitry, the feature vector using a machine learning model to produce a machine learning model output;
and
causing to be provided a recommended content item based on the machine learning model output.

3 more elements

Dependencies are inferred from the claim text.

Systems and methods for deep recommendations using signature analysis

Life of this patent

Systems and methods for deep recommendations using signature analysis

Life of this patent

Abstract

Claims21